arXiv 论文提出 MSS 距离度量，量化保险定价透明模型的结构稳定性
Quantifying structural stability of transparent models for general insurance pricing
做保险定价或风控建模的值得看这篇：作者提出 MSS 距离来衡量模型重训前后结构变化，比只看预测稳定性更上游。论文用三个车险数据集实测，GLM 最稳，EBM 兼顾稳定和准确，还给 EBM 和 XGBoost 配了固定超参数等稳定化技巧，代价几乎为零。精算和合规团队可以直接借鉴这套评估流程。
arXiv 论文 2611.17308 针对通用保险（general insurance）合同周期短、数据分布快速漂移导致模型频繁更新的问题，提出了功能透明（functional transparency）模型的概念，即预测函数可表示为主效应单变量函数与两两交互双变量函数之和的模型。
论文进一步提出 Mean Squared Shape function distance（MSS 距离），用于量化这类透明模型在更新前后的结构稳定性，弥补现有结构稳定性指标大多只适用于简单决策树的不足。作者在三个公开车险数据集上做了基准研究：中心化数值协变量的广义线性模型（GLM）最稳定，explainable boosting machines（EBM）则在稳定性与预测性能之间取得折中。
研究还发现，顺序建模、固定超参数等稳定化策略能显著提升 EBM 与 XGBoost 的结构稳定性，且对性能的影响可以忽略。对需要在监管约束下定期重训定价模型的精算团队来说，这套度量提供了一条筛选“既准又稳”模型的可操作路径。
Quantifying structural stability of transparent models for general insurance pricing
The short duration of general insurance contracts leads to rapid distributional shifts in the data, warranting regular model updates. This process may yield a post-update model that differs considerably from the pre-update model in both predictions and structure. Structural stability is upstream of predictive stability, yet existing structural stability metrics are mostly restricted to simple decision trees. Thus, we first introduce the notion of functional transparency, characterising models with a prediction function that can be expressed as a sum of univariate functions for main effects and bivariate functions for pairwise interactions. Then, we propose the Mean Squared Shape function distance, a metric to quantify the structural stability of functionally transparent models, which are particularly relevant in actuarial science. Using three public auto insurance datasets, we present a benchmark study to demonstrate the usefulness of our metric in identifying a stable and accurate transparent model. Generalised linear models with centred numerical covariates are stable, and explainable boosting machines (EBMs) offer a compromise between stability and performance. We also find that stabilisation strategies, such as sequential modelling and fixed hyperparameters, can substantially improve stability for both EBM and XGBoost models, with negligible impact on performance.
本条情报归属 AITOP「论文」核心栏目，重点聚焦欧洲人工智能治理与技术生态。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：保险定价、EBM、XGBoost、精算模型，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Félix Côté, Marie-Pier Côté），经 AITOP 多维评分管线结构化提炼。